TinySurveillance: An Extra Low-Power Event-Based Surveillance Method for UAVs
Bibliographic record
Abstract
Unmanned Aerial Vehicles (UAVs) have always been faced with power management challenges to extend their flight time. Managing power consumption becomes critical, especially in surveillance applications, where the longer flight time results in wider coverage and a cheaper solution. Most of the current studies show new methods for event detection without considering power consumption. This article presents an event-driven four-stage video surveillance pipeline with an efficient video transmission algorithm balancing power consumption and image quality. The surveillance starts automatically when the low-power AI-based onboard processor detects the desired event. When The edge node detects the defined event, a sample image is sent to the server for validation. After validation, a colored image accompanied by <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> grayscale images are sent to the server. The server colorizes the grayscale images using a convolutional neural network trained by the colored images. In this work, an application of wildfire detection and surveillance has been implemented to show the proof of concept of the TinySurveillance method. The results show that the power consumption of the onboard processing unit in detection mode reduces by at least 4 times; during the surveillance mode, the data transmission rate can be decreased by almost 66% while achieving a competent image quality PSNR<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Avg</sub> of 41.35 dB, PSNR of 30.94 dB, and output frame rate of 5.2.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".